SoroushVahidi/scidocs huggingface dataset:
Multilevel Converters as a Utility Interface for Renewable Energy Systems
(2000)

Electric power production in the 21 Century will see dramatic changes in both the physical infrastructure and the control and information infrastructure. A shift will take place from a relatively few large, concentrated generation centers and the transmission of electricity over mostly a high voltage ac grid (Fig. 1) to a more diverse and dispersed generation infrastructure that also has a higher percentage of dc transmission lines (Fig. 2) [1]. In the United States, generation capacity has not kept up with power demands, as over the last decade the reserve margins declined from 22% in 1990 to 16% in 1997. This declining margin trend is expected to continue over the next decade partly because of the uncertainty of how the future deregulated electrical environment will function. Less reserve margins will lead to less peaking capacity on highdemand days and more volatile energy prices [2]. This change in a physical infrastructure combined with a more deregulated electric power industry will result in more parties generating power – or distributed generation. Some of the distributed generation power sources that are expected to increase greatly their market share of the total power produced in the United States and abroad include renewable energy sources such as photovoltaics, wind, low-head hydro, and geothermal [3]. Fuel cell technology is also nearing the development point where it could start to supply a significant share of the power needs [4]. The advent of high power electronic modules has also encouraged the use of more dc transmission and made the prospects of interfacing dc power sources such as fuel cells and photovoltaics more easily attainable. A modular, scalable power electronics technology that is ideal for these types of utility applications is the transformerless multilevel converter [5].
Cascaded DC-DC converter connection of photovoltaic modules
(2004)

New residential scale photovoltaic (PV) arrays are commonly connected to the grid by a single dc-ac inverter connected to a series string of pv panels, or many small dc-ac inverters which connect one or two panels directly to the ac grid. This paper proposes an alternative topology of nonisolated per-panel dc-dc converters connected in series to create a high voltage string connected to a simplified dc-ac inverter. This offers the advantages of a "converter-per-panel" approach without the cost or efficiency penalties of individual dc-ac grid connected inverters. Buck, boost, buck-boost, and Cu/spl acute/k converters are considered as possible dc-dc converters that can be cascaded. Matlab simulations are used to compare the efficiency of each topology as well as evaluating the benefits of increasing cost and complexity. The buck and then boost converters are shown to be the most efficient topologies for a given cost, with the buck best suited for long strings and the boost for short strings. While flexible in voltage ranges, buck-boost, and Cu/spl acute/k converters are always at an efficiency or alternatively cost disadvantage.
An FPGA-based AES-CCM Crypto Core For IEEE 802.11i Architecture
(2007)

The widespread adoption of IEEE 802.11 wireless networks has brought its security paradigm under active research. One of the important research areas in this field is the realization of fast and secure implementations of cryptographic algorithms. Under this work, such an implementation has been done for Advanced Encryption Standard (AES) on fast, efficient and low power Field Programmable Gate Arrays (FPGAs) whereby computational intensive cryptographic processes are offloaded from the main processor thus results in achieving highspeed secure wireless connectivity. The dedicated resources of Spartan-3 FPGAs have been effectively utilized to develop wider logic function which minimizes the critical paths by confining logic to single Configurable Logic Block (CLB), thus improving the performance, density and power consumption of the design. The resultant design consumes only 4 Block RAMs and 487 Slices to fit both AES cores and its key scheduling.
Learning an Intrinsic Image Decomposer Using Synthesized RGB-D Dataset
(2018)

Intrinsic image decomposition refers to recover the albedo and shading from images, which is an ill-posed problem in signal processing. As realistic labeled data are severely lacking, it is difficult to apply learning methods in this issue. In this letter, we propose using a synthesized dataset to facilitate the solving of this problem. A physically based renderer is used to generate color images and their underlying ground-truth albedo and shading from three-dimensional models. Additionally, we render a Kinect-like noisy depth map for each instance. We utilize this synthetic dataset to train a deep neural network for intrinsic image decomposition and further fine-tune it for real-world images. Our model supports both RGB and RGB-D as input, and it employs both high-level and low-level features to avoid blurry outputs. Experimental results verify the effectiveness of our model on realistic images.
Survey: Finite-state technology in natural language processing
(2017)

In this survey, we will discuss current uses of finite-state information in several statistical natural language processing tasks. To this end, we will review standard approaches in tokenization, part-of-speech tagging, and parsing, and illustrate the utility of finite-state information and technology in these areas. The particular problems were chosen to allow a natural progression from simple prediction to structured prediction. We aim for a sufficiently formal presentation suitable for readers with a background in automata theory that allows to appreciate the contribution of finite-state approaches, but we will not discuss practical issues outside the core ideas. We provide instructive examples and pointers into the relevant literature for all constructions. We close with an outlook on finite-state technology in statistical machine translation.
Design and performance analysis of a high-speed air-cored axial-flux permanent-magnet generator with circular magnets and coils
(2009)

Air-cored, axial flux permanent-magnet (AFPM) machines have magnetic and mechanical characteristics considered to be ideal for compact high speed electric power generation applications. To date, research on high speed AFPM machines is primarily from a mechanical perspective, with emphasis on higher power level applications such as in hybrid vehicles. Recently, there has been an increasing interest in man-portable mobile power sources with a power envelope of around 1kW, for mission critical applications. In this paper, a high speed air-cored surface mounted AFPM machine with circular magnets and coils is proposed. Comprehensive theoretical analysis and three-dimensional (3-D) electromagnetic finite element analysis (FEA) are developed to evaluate the performance of the machine. In addition, the rotor's mechanical stresses developed by high rotational speed are evaluated to ensure mechanical integrity. Finally, a prototype machine is developed for validation. Both the experimental and predicted results demonstrate that the proposed generator possesses distinct advantages over other systems, such as high efficiency, high power factor, and a simple and robust structure that offers a high degree of technology readiness.
PeerFlow: Secure Load Balancing in Tor
(2017)

We present PeerFlow, a system to securely load balance client traffic in Tor. Security in Tor requires that no adversary handle too much traffic. However, Tor relays are run by volunteers who cannot be trusted to report the relay bandwidths, which Tor clients use for load balancing. We show that existing methods to determine the bandwidths of Tor relays allow an adversary with little bandwidth to attack large amounts of client traffic. These methods include Tor’s current bandwidthscanning system, TorFlow, and the peer-measurement system EigenSpeed. We present an improved design called PeerFlow that uses a peer-measurement process both to limit an adversary’s ability to increase his measured bandwidth and to improve accuracy. We show our system to be secure, fast, and efficient. We implement PeerFlow in Tor and demonstrate its speed and accuracy in large-scale network simulations.
Sentiment Analysis on Twitter Data
(2015)

Abstract – Now-a-days social networking sites are at the boom, so large amount of data is generated. Millions of people are sharing their views daily on micro blogging sites, since it contains short and simple expressions. In this paper, we will discuss about a paradigm to extract the sentiment from a famous micro blogging service, Twitter, where users post their opinions for everything. In this paper, we will discuss the existing analysis of twitter dataset with data mining approach such as use of Sentiment analysis algorithm using machine learning algorithms. An approach is introduced that automatically classifies the sentiments of Tweets taken from Twitter dataset as in [1]. These messages or tweets are classified as positive, negative or neutral with respect to a query term. This is very useful for the companies who want to know the feedback about their product brands or the customers who want to search the opinion from others about product before purchase. We will use machine learning algorithms for classifying the sentiment of Twitter messages using distant supervision which is discussed in [8]. The training data consists of Twitter messages with emoticons, acronyms which are used as noisy labels discussed in [4]. We examine sentiment analysis on Twitter data. The contributions of this survey paper are: (1) we use Parts Of Speech (POS)-specific prior polarity features. (2) We also use a tree kernel to prevent the need for monotonous feature engineering.
Arrhythmia Identification with Two-Lead Electrocardiograms Using Artificial Neural Networks and Support Vector Machines for a Portable ECG Monitor System
(2013)

An automatic configuration that can detect the position of R-waves, classify the normal sinus rhythm (NSR) and other four arrhythmic types from the continuous ECG signals obtained from the MIT-BIH arrhythmia database is proposed. In this configuration, a support vector machine (SVM) was used to detect and mark the ECG heartbeats with raw signals and differential signals of a lead ECG. An algorithm based on the extracted markers segments waveforms of Lead II and V1 of the ECG as the pattern classification features. A self-constructing neural fuzzy inference network (SoNFIN) was used to classify NSR and four arrhythmia types, including premature ventricular contraction (PVC), premature atrium contraction (PAC), left bundle branch block (LBBB), and right bundle branch block (RBBB). In a real scenario, the classification results show the accuracy achieved is 96.4%. This performance is suitable for a portable ECG monitor system for home care purposes.
Implementation of a Cognitive Radio Front-End Using Rotatable Controlled Reconfigurable Antennas
(2011)

This communication presents a new antenna system designed for cognitive radio applications. The antenna structure consists of a UWB antenna and a frequency reconfigurable antenna system. The UWB antenna scans the channel to discover “white space” frequency bands while tuning the reconfigurable section to communicate within these bands. The frequency agility is achieved via a rotational motion of the antenna patch. The rotation is controlled by a stepper motor mounted on the back of the antenna structure. The motor's rotational motion is controlled by LABVIEW on a computer connected to the motor through its parallel port. The computer's parallel port is connected to a NPN Darlington array that is used to drive the stepper motor. The antenna has been simulated with the driving motor being taken into consideration. A good agreement is found between the simulated and the measured antenna radiation properties.
A new ultra-wideband, ultra-short monocycle pulse generator with reduced ringing
(2002)

We introduce a new ultra-wideband (UWB), ultra-short, step recovery diode monocycle pulse generator. This pulse generator uses a simple RC high-pass filter as a differentiator to generate the monocycle pulse directly. The pulse-shaping network employs a resistive circuit to achieve UWB matching and substantial removal of the pulse ringing, and rectifying and switching diodes to further suppress the ringing. An ultra-short monocycle pulse of 300-ps pulse duration, -17-dB ringing level, and good symmetry has been demonstrated. Good agreement between the measured and calculated results was achieved.
EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks
(2009)

In recent years, there has been a growing interest in wireless sensor networks. One of the major issues in wireless sensor network is developing an energy-efficient clustering protocol. Hierarchical clustering algorithms are very important in increasing the network’s life time. Each clustering algorithm is composed of two phases, the setup phase and steady state phase. The hot point in these algorithms is the cluster head selection. In this paper, we study the impact of heterogeneity of nodes in terms of their energy in wireless sensor networks that are hierarchically clustered. We assume that a percentage of the population of sensor nodes is equipped with the additional energy resources. We also assume that the sensor nodes are randomly distributed and are not mobile, the coordinates of the sink and the dimensions of the sensor field are known. Homogeneous clustering protocols assume that all the sensor nodes are equipped with the same amount of energy and as a result, they cannot take the advantage of the presence of node heterogeneity. Adapting this approach, we introduce an energy efficient heterogeneous clustered scheme for wireless sensor networks based on weighted election probabilities of each node to become a cluster head according to the residual energy in each node. Finally, the simulation results demonstrate that our proposed heterogeneous clustering approach is more effective in prolonging the network lifetime compared with LEACH. 2008 Elsevier B.V. All rights reserved.
Online Adaptive Passive-Aggressive Methods for Non-Negative Matrix Factorization and Its Applications
(2016)

This paper aims to investigate efficient and scalable machine learning algorithms for resolving Non-negative Matrix Factorization (NMF), which is important for many real-world applications, particularly for collaborative filtering and recommender systems. Unlike traditional batch learning methods, a recently proposed online learning technique named "NN-PA" tackles NMF by applying the popular Passive-Aggressive (PA) online learning, and found promising results. Despite its simplicity and high efficiency, NN-PA falls short in at least two critical limitations: (i) it only exploits the first-order information and thus may converge slowly especially at the beginning of online learning tasks; (ii) it is sensitive to some key parameters which are often difficult to be tuned manually, particularly in a practical online learning system. In this work, we present a novel family of online Adaptive Passive-Aggressive (APA) learning algorithms for NMF, named "NN-APA", which overcomes two critical limitations of NN-PA by (i) exploiting second-order information to enhance PA in making more informative updates at each iteration; and (ii) achieving the parameter auto-selection by exploring the idea of online learning with expert advice in deciding the optimal combination of the key parameters in NMF. We theoretically analyze the regret bounds of the proposed method and show its advantage over the state-of-the-art NN-PA method, and further validate the efficacy and scalability of the proposed technique through an extensive set of experiments on a variety of large-scale real recommender systems datasets.
Network-based wireless location: challenges faced in developing techniques for accurate wireless location information
(2005)

Wireless location refers to the geographic coordinates of a mobile subscriber in cellular or wireless local area network (WLAN) environments. Wireless location finding has emerged as an essential public safety feature of cellular systems in response to an order issued by the Federal Communications Commission (FCC) in 1996. The FCC mandate aims to solve a serious public safety problem caused by the fact that, at present, a large proportion of all 911 calls originate from mobile phones, the location of which cannot be determined with the existing technology. However, many difficulties intrinsic to the wireless environment make meeting the FCC objective challenging. These challenges include channel fading, low signal-to-noise ratios (SNRs), multiuser interference, and multipath conditions. In addition to emergency services, there are many other applications for wireless location technology, including monitoring and tracking for security reasons, location sensitive billing, fraud protection, asset tracking, fleet management, intelligent transportation systems, mobile yellow pages, and even cellular system design and management. This article provides an overview of wireless location challenges and techniques with a special focus on network-based technologies and applications.
Microstrip Antennas
(2004)

Microstrip antennas have been one of the niost innovative topics in antenna theory and design in recent years, and are increasingly finding application in a wide range of modern microwave systems. This paper begins with a brief overview of the basic characteristics of microstrip antennas, and then concentrates on the most significant developments in microstrip antenna technology that have been made in the last several years. Emphasis is on new antenna configurations for improved electrical performance and manufacturability, and advances in the analytical modeling of microstrip antennas and arrays.
Environmental impacts of polyvinyl chloride (PVC) production process
(2015)

The increasing demand for plastics in the world leads to pressures on the environment generated by the consumption of raw materials based on fossil fuels and the need for plastic waste reduction and absorption to diminish their environmental impacts. In this paper we evaluated the environmental impacts induced by the production of polyvinyl chloride (PVC), one of the most widely used plastics in the manufacture of various goods. The impacts were calculated based on Life Cycle Assessment methodology. The negative environmental impacts of PVC production may be reduced by recycling, a sustainable, effective, currently available alternative of waste management. Our study showed that the use of recyclable materials, such as organic biodegradable waste as raw materials instead of crude oil can lead to significant reduction of environmental impacts.
On Networking of Internet of Things: Explorations and Challenges
(2016)

Internet of Things (IoT), as the trend of future networks, begins to be used in many aspects of daily life. It is of great significance to recognize the networking problem behind developing IoT. In this paper, we first analyze and point out the key problem of IoT from the perspective of networking: how to interconnect large-scale heterogeneous network elements and exchange data efficiently. Combining our on-going works, we present some research progresses on three main aspects: 1) the basic model of IoT architecture; 2) the internetworking model; and 3) the sensor-networking mode. Finally, we discuss two remaining challenges in this area.
A Systematic Review of Service Level Management in the Cloud
(2015)

Cloud computing make it possible to flexibly procure, scale, and release computational resources on demand in response to workload changes. Stakeholders in business and academia are increasingly exploring cloud deployment options for their critical applications. One open problem is that service level agreements (SLAs) in the cloud ecosystem are yet to mature to a state where critical applications can be reliably deployed in clouds. This article systematically surveys the landscape of SLA-based cloud research to understand the state of the art and identify open problems. The survey is particularly aimed at the resource allocation phase of the SLA life cycle while highlighting implications on other phases. Results indicate that (i) minimal number of SLA parameters are accounted for in most studies; (ii) heuristics, policies, and optimisation are the most commonly used techniques for resource allocation; and (iii) the monitor-analysis-plan-execute (MAPE) architecture style is predominant in autonomic cloud systems. The results contribute to the fundamentals of engineering cloud SLA and their autonomic management, motivating further research and industrial-oriented solutions.
Logo Retrieval Using Logo Proposals and Adaptive Weighted Pooling
(2017)

This letter presents a novel approach for logo retrieval. Considering the fact that logo only occupies a small portion of an image, we apply Faster R-CNN to detect logo proposals first, and then use a two-step pooling strategy with adaptive weight to obtain an accurate global signature. The adaptive weighted pooling method can effectively balance the recall and precision of proposals by incorporating the probability of each proposal being a logo. Experimental results show that the proposed method interprets the similarity between query and database image more accurately and achieves state of the art performance.
Monolithically Integrated Si Photonics Transmitters in 0.25 um BiCMOS Platform for High-Speed Optical Communications
(2018)

Monolithically integrated electro-optical transmitters fabricated in a 0.25 μm SiGe:C BiCMOS electronic-photonic integrated circuit (EPIC) technology with fT =190 GHz are presented. The modules are based on the co-design and integration of a segmented depletion-type Si Mach-Zehnder modulator and multichannel driver amplifiers. Two driving approaches and their performance trade-offs are discussed: a linear one, with direct interface to an external digital-to-analog converter (DAC), as well as a more power efficient implementation featuring integrated 4-bit DAC functionality. High extinction ratios at high speed are demonstrated, enabled by the high breakdown voltages of the HBT transistors. The modules support several modulation formats among which pulse-amplitude-modulation (PAM)−4 eye diagrams up to 37 GBd are shown, for the first time.
IT Outsourcing Strategies: Universalistic, Contingency, and Configurational Explanations of Success
(2004)

F on individual outsourcing decisions in IT research has often yielded contradictory findings and recommendations. To address these contradictions, we investigate a holistic, configurational approach with the prevailing universalistic or contingency perspectives in exploring the effects of IT outsourcing strategies on outsourcing success. Based on residual rights theory, we begin by identifying three dimensions of IT outsourcing strategies: degree of integration, allocation of control, and performance period. We then develop a model of fit-as-gestalt, drawing from literatures on strategy, governance, interorganizational relationships, and outsourcing. Next, based on data from 311 firms in South Korea, we test universalistic and contingency perspectives in explaining the relationship between IT outsourcing strategies and outsourcing success. We then identify three congruent patterns, or gestalts, of IT outsourcing strategies. We term these strategies independent, arm’s-length, and embedded strategies. To establish the predictive validity of these gestalts and the viability of a configurational perspective, we then explore the effects of these congruent gestalts vis-à-vis noncongruent patterns on three dimensions of outsourcing success: strategic competence, cost efficiency, and technology catalysis. We also contrast the effects of each of the three gestalts on each of the three dimensions of outsourcing success. Our findings indicate the superiority of the configurational approach over universalistic and contingency perspectives in explaining outsourcing success.